system

Abstract

We provide the system. [Solution] Means for collecting personal dietary history and health data, A means for analyzing the collected data to profile an individual's nutritional status and food preferences, A means for generating a meal menu that matches nutritional balance and individual preferences based on the aforementioned profiling results, A means for presenting the generated menu to an individual, A means of collecting individual feedback on the aforementioned menu and utilizing it for generating the next menu, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern busy lives, many people have difficulty choosing a healthy diet. In particular, choosing an optimal menu according to individual nutritional status and food preferences requires expertise and is time-consuming. Also, since it is difficult to prepare a balanced meal within limited time, generally the nutritional balance tends to be uneven. This invention aims to improve such a situation and provide support for easily realizing a healthy diet suitable for individuals.

Means for Solving the Problems

[0005] This invention provides a system that collects an individual's dietary history and health data, analyzes it, and profiles the individual's nutritional status and food preferences. Based on the profiling results, the system uses AI to generate meal menus that match the nutritional balance and the individual's preferences. The generated menus are then presented to the individual, and feedback from the individual is collected and incorporated into the next menu generation to provide more optimized suggestions. Furthermore, the menus take into account the individual's allergy information and incorporate nutritional guidelines to support safe and healthy meal choices. As a result, users can easily and efficiently choose meals that are appropriate for their own health condition.

[0006] "Personal dietary history" refers to a record of the types, frequency, and quantity of meals a particular individual has consumed in the past.

[0007] "Health data" refers to information about an individual's health status, including health checkup results, allergy information, and whether or not they have pre-existing conditions.

[0008] "Profiling" is the process of analyzing collected data to identify individual characteristics such as nutritional status and food preferences.

[0009] "Nutritional balance" refers to a state in which an individual's diet contains the necessary nutrients (carbohydrates, proteins, fats, vitamins, minerals, etc.) in appropriate proportions.

[0010] "Menu generation" is the process of combining and suggesting meal options based on specific conditions.

[0011] "Feedback" is the act of an individual communicating their results and impressions to the system after trying a menu item that has been presented to them.

[0012] "Allergy information" refers to data about hypersensitivity reactions that individuals exhibit to specific foods or ingredients.

[0013] "Nutritional guidelines" are standard nutritional intake criteria or guidelines for constructing a healthy diet. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system that provides meal suggestions that take into account an individual's nutritional status and preferences. The specific operation of the system is described below.

[0036] First, the terminal serves as a receiver for user input. Users input their eating history, health checkup results, allergy information, and other data into the system via the terminal. This information becomes the basic data for analyzing an individual's nutritional status and food preferences.

[0037] Next, this input data is sent to the server. Based on the received data, the server uses an AI algorithm to create an individual nutrition profile. The nutrition profile includes the user's past eating habits, nutrient deficiencies or excesses, and allergy risks.

[0038] The AI ​​model automatically generates personalized meal plans based on this nutritional profile. The generated menus consider a healthy nutritional balance and are tailored to the user's preferences and allergy information.

[0039] Next, the generated menu is sent to the terminal and presented to the user. The user can then procure ingredients and cook according to the presented menu. The menu also includes nutritional information and information on potential convenience store items to support smooth ingredient procurement.

[0040] After a user tries a menu item, they input their results and impressions as feedback. This feedback is sent back to the server, and the system uses this information to adjust the AI ​​algorithm. This is to enable the system to suggest more personalized content for future menu generation.

[0041] As a concrete example, when user A uses the system, the server analyzes the user's dietary history to determine that they are lacking protein and suggests a "healthy bowl using tofu." Furthermore, depending on whether or not the user has a dairy allergy, alternative dairy-free products are added to their shopping list at the convenience store.

[0042] In this way, the system suggests meals best suited to individual circumstances, helping users easily maintain a healthy diet. This invention provides a groundbreaking means of conveniently realizing a healthy diet for people.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] Users log in to their devices and enter their meal history, health checkup results, and allergy information. This entered data is linked to the user's individual account.

[0046] Step 2:

[0047] The terminal sends the entered data to the system's server. The data is encrypted and transmitted securely.

[0048] Step 3:

[0049] The server stores the received data in a database and analyzes it using AI algorithms. Specifically, it profiles the user's nutritional status and food preferences to identify nutrient deficiencies and excesses.

[0050] Step 4:

[0051] Based on the analysis results, the server generates a nutritionally balanced meal menu suitable for the user. The algorithm constructs the menu considering the nutritional value of ingredients, the user's preferences, and allergy information.

[0052] Step 5:

[0053] The server sends the generated menu to the terminal. The transmitted data includes nutritional information and a list of related products that can be purchased at convenience stores.

[0054] Step 6:

[0055] The terminal displays the received menu information to the user. The user reviews the displayed menu, purchases ingredients as needed, and prepares the meal.

[0056] Step 7:

[0057] After the user tries the suggested menu items, they input feedback about the results and their satisfaction level into the device.

[0058] Step 8:

[0059] The device sends user feedback to the server. Based on this feedback, the server updates the AI ​​model to improve accuracy in future menu generation.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] In modern society, it is extremely difficult to propose nutritionally balanced meals tailored to individual health conditions and preferences. Moreover, for people leading busy lives, it is difficult to easily obtain information on how to select and purchase appropriate ingredients. As a result, there is a growing problem of an increasing number of people not getting enough nutrients necessary for maintaining good health. Furthermore, those with allergies face the risk of accidentally consuming dangerous ingredients. Therefore, there is a need for a system that can automatically generate appropriate meal menus, taking into account an individual's nutritional status and food preferences, and provide the necessary information.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for acquiring meal history and health information, means for analyzing the acquired data to design a nutritional profile, and means for generating meal menus using a generation AI model based on the nutritional profile. This enables the automatic provision of nutritionally balanced meal menus tailored to individual needs, thereby realizing a healthy lifestyle.

[0065] "Individual" refers to a specific person, and the subject of creating a nutritional profile based on that person's dietary history and health information.

[0066] A "nutritional profile" is a dataset that comprehensively includes information on nutrient deficiencies and excesses, food preferences, and allergies, analyzed using an individual's dietary history and health information.

[0067] A "generative AI model" refers to an algorithm that automatically generates meal menus that are balanced nutritionally and tailored to individual preferences, based on an individual's nutritional profile.

[0068] A "meal menu" refers to a personalized list of specific foods and dishes created by a generative AI model, taking into account nutritional value and personal preferences.

[0069] "Responses" refer to the opinions and evaluations that individuals give regarding the presented meal menu, and this information is collected to help create future menus.

[0070] "Product information" refers to detailed data about purchasable products related to meal menus, and is information that can be used as a reference when individuals procure appropriate ingredients.

[0071] This invention is a system that proposes customized meal menus based on an individual's nutritional status and food preferences. Specific embodiments of the system are described below.

[0072] The terminal functions as a device for receiving data input from users. Users input their meal history, health checkup data, and allergy information through an interface on the terminal. This interface is implemented using a dedicated application or web-based form and is designed to be easy for users to use. The data entered from the terminal is converted into a structured data format, such as JSON format, and sent to the server using a secure protocol.

[0073] The server processes the received data and performs analysis through AI algorithms. Specifically, the AI ​​model is developed in Python and utilizes libraries such as TENSORFLOW® or PyTorch. This model generates a nutritional profile and creates menus that reflect the user's eating habits, nutrient deficiencies or excesses, and allergy information. At this time, the server accesses a database to refer to the latest nutritional data and ingredient information.

[0074] The generated menus are tailored to nutritional balance and individual preferences, and also include information on available products. For example, a user lacking protein might be suggested a "nutritious dish using tofu," and alternative ingredients would be suggested based on allergy information. As a concrete example, a prompt used in the generating AI model might be something like, "Generate a recommended meal plan for the next week based on the user's eating history and health data."

[0075] Finally, the generated menu is sent back to the terminal. The user can immediately view the menu and efficiently procure the necessary ingredients by referring to the purchasing information associated with the menu.

[0076] In this way, the system provides meal suggestions tailored to specific users, supporting them in achieving a healthy diet.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The terminal receives input data from the user. Here, the user enters their meal history, health status information, and allergy information via an application or web interface. This input data is processed into JSON format. The final output is structured data that is securely sent to the server.

[0080] Step 2:

[0081] The server receives data sent from the terminal. The server parses the received JSON data and starts creating a nutritional profile by comparing it with the database. Based on the input data, it calculates nutrient deficiencies or excesses and allergy risks. The output of this analysis is a detailed dataset that constitutes the individual's nutritional profile.

[0082] Step 3:

[0083] The server's AI algorithm uses a generated AI model to receive a nutritional profile as input and generate a meal plan suitable for the user. This process uses prompts to generate the menu. For example, the prompt might be "Create a healthy one-week menu based on user A's nutritional status." The output is a meal plan that balances nutritional value with personal preferences.

[0084] Step 4:

[0085] The server sends the generated menu to the terminal in structured data format. The terminal parses the received data and displays it in a user-friendly interface. As output, the user can view a detailed meal menu and related purchase information.

[0086] Step 5:

[0087] After the user prepares the meal according to the provided menu, they input feedback into the system via their device. This feedback includes their impressions of the dish they actually prepared and suggestions for improvement. The device then processes this information back into JSON format and sends it to the server.

[0088] Step 6:

[0089] The server analyzes user feedback and incorporates it into the AI ​​algorithm to improve the system's accuracy. Based on the input feedback, adjustments are made to inform the next menu suggestion. This output is adjustment data, including suggestions for improvement for the next suggestion.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] In today's busy lifestyle, selecting and preparing appropriate meals based on individual health conditions and nutritional balance is difficult for many people. As a result, health problems such as nutritional excess, deficiency, and allergic reactions are common. Furthermore, finding meals that suit individual preferences is not easy. There is a need to solve these problems and provide healthy meals that are tailored to each individual.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for collecting an individual's dietary history and health data, means for analyzing the collected data to profile the individual's nutritional status and food preferences, and means for generating a meal menu that matches nutritional balance and individual preferences based on the profiling results. This makes it possible to select and provide meals that meet individual needs.

[0095] "Dietary history" refers to information that records the content, frequency, and timing of meals an individual has consumed in the past.

[0096] "Health data" refers to information that represents an individual's health status, and includes results from health checkups and daily health management data.

[0097] "Profiling" refers to the process of analyzing collected data to understand an individual's preferences and tendencies, and then creating a model based on that understanding.

[0098] "Nutritional balance" refers to a state in which the human body is consuming the necessary nutrients in appropriate proportions.

[0099] A "meal menu" is a list that shows the contents and menu of a meal, and is structured according to a specific purpose or condition.

[0100] "Meal service" refers to the business or activity of providing meals and ingredients, and arranging food according to the needs of the users.

[0101] "Communication methods" refer to the technical mechanisms for exchanging data and information between various devices and systems.

[0102] This invention is a system that supports a healthy diet tailored to each individual. The server receives meal history and health data transmitted by the user and uses an AI algorithm to generate an individual nutritional profile based on this data. This AI model takes into particular consideration nutritional balance, individual food preferences, and allergy information. Based on the generated profile, the server creates a meal menu optimized for the individual's needs and transmits the menu information to the user's terminal via communication means.

[0103] The user's device uses a cross-platform mobile application built with React Native to browse menus and order meals in conjunction with the food service options offered. The backend system is built with Node.js and Express to manage user data and information from service providers. A MongoDB database enables the continuous accumulation of individual nutritional profiles and menu information.

[0104] As a concrete example, when a user enters their meal history and health checkup results through the application, the server analyzes for vitamin D deficiency and suggests "grilled salmon and spinach." The system then orders the dish through a partner restaurant via delivery service and delivers it to the user.

[0105] An example of a prompt for a generative AI model would be: "Analyze the user's dietary history to identify any nutritional deficiencies and suggest a menu if the user is deficient in vitamin D. However, the user has a shellfish allergy."

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] Users use their devices to input their meal history and health data.

[0109] The input data includes past meal history, health checkup results, and allergy information. The terminal sends this data to the server. As output, the user's individual data is stored on the server.

[0110] Step 2:

[0111] The server analyzes the received data to profile the individual's nutritional status and food preferences.

[0112] The input is data submitted by the user, and the output is an individual nutritional profile. The server uses AI algorithms to perform data analysis and profiling. Generative AI models are utilized in this process.

[0113] Step 3:

[0114] The server generates a meal plan that takes nutritional balance into consideration, based on the profiling results.

[0115] The input is a nutritional profile, and the output is a meal menu. The server inputs prompts into a generative model and automatically generates the menu.

[0116] Step 4:

[0117] The server sends the generated menu to the terminal.

[0118] As output, a personalized meal menu is displayed on the user's device. The user can select their preferred items from these menus.

[0119] Step 5:

[0120] Users select menu items via their terminal and place orders through the corresponding food service.

[0121] The input is the user's menu selection, and the output is the transmission of the selection information to the food service. The selected menu item is then ordered through integration with the service provider's system.

[0122] Step 6:

[0123] Users actually receive the food and provide feedback.

[0124] The input is feedback based on the experience of the provided dish, and the output is the transmission of this new feedback data to the server. This feedback will be used in the next menu generation process.

[0125] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0126] This invention relates to a system that provides personalized meal suggestions, taking into account a user's eating history, health data, and emotional state. The system aims to provide menus that satisfy not only the user's nutritional needs but also their emotional needs.

[0127] First, the device receives input from the user. At this time, the user inputs not only their meal history and health data, but also their emotional state in real time through an emotion engine. This emotional information is collected through voice and facial expression analysis.

[0128] The received data is sent to a server. The server analyzes the data using an AI algorithm and creates an individualized nutritional profile. This profile combines the user's daily eating habits, health status, and even emotional data.

[0129] Next, the server's algorithm generates a meal menu tailored to the user. This process considers not only nutritional balance but also ingredients and cooking methods that address the user's emotional state. For example, it might suggest a relaxing herbal tea when the user is under high stress.

[0130] The generated menu is then presented to the user again via the terminal. The menu includes detailed information such as necessary nutritional information, a list of items that can be purchased at convenience stores, and the effects of ingredients that correspond to emotions.

[0131] After users actually try the menu, they input their results and impressions into the system as feedback. This feedback is based on changes in the user's emotions and their satisfaction with the meal.

[0132] Finally, the server updates the AI ​​model based on the feedback received, improving the accuracy of future menu suggestions. In this way, the system is optimized for each user, supporting a highly satisfying eating lifestyle not only nutritionally but also emotionally.

[0133] As a concrete example, when user B logs in, it is discovered that their mood is unstable that day. As a result, the server suggests oatmeal, which has a calming effect, and a fruit salad containing vitamin C, which can boost mood. In this way, a comprehensive meal recommendation that takes emotional state into consideration is realized.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The device accepts input from the user. The user inputs their meal history, health data, and emotional information. The emotional information is processed in real time by an emotion engine, which analyzes voice and facial expressions via microphones and cameras to identify emotions.

[0137] Step 2:

[0138] The terminal encrypts all entered data before sending it to the server for security purposes. This ensures that data transfer is performed while protecting user privacy.

[0139] Step 3:

[0140] The server uses AI algorithms to analyze the received data. Based on meal history and health data, nutritional status is analyzed, and at the same time, an emotion engine evaluates the user's emotional state, creating an integrated nutritional profile.

[0141] Step 4:

[0142] The server generates meal plans based on the generated nutritional profile. It proposes individually optimized menus that incorporate nutritional balance, preferred ingredients, and specific foods and ingredients that suit the user's emotional state.

[0143] Step 5:

[0144] The server sends the created menu back to the terminal. The terminal provides the user with menu details, nutritional information, and reasoning behind ingredient selections based on emotions. A list of items available at convenience stores is also attached.

[0145] Step 6:

[0146] Users eat according to the provided meal menu and observe changes in their emotions and level of satisfaction. After finishing the meal, users input feedback about their satisfaction and emotional changes into the device.

[0147] Step 7:

[0148] The device securely sends feedback to the server. The server uses this feedback to update the AI ​​algorithm model and adapt it to further improve accuracy in future menu generation.

[0149] Through this series of steps, the system becomes capable of providing more personalized menus tailored to each user's unique nutritional status and emotional state.

[0150] (Example 2)

[0151] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0152] When suggesting meals, it is desirable to consider not only individual health conditions and food preferences but also emotional states. However, conventional systems have the challenge of making personalized suggestions that reflect emotions. Furthermore, in order to improve the accuracy and satisfaction of meal suggestions, a system that efficiently utilizes individual feedback is required.

[0153] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0154] In this invention, the server includes means for collecting an individual's eating history, health status, and emotional state; means for analyzing the collected data to profile the individual's nutritional status, food preferences, and emotional state; and means for updating an artificial intelligence model based on the individual's feedback. This enables a high level of personalization that takes into account the individual's health status, food preferences, and emotional trends, thereby increasing user satisfaction.

[0155] A "dietary history" is a collection of information about the types, times, and frequency of meals an individual has eaten in the past.

[0156] "Health status" refers to information about an individual's physical health, including medical history, allergy information, and anthropometric data.

[0157] "Emotional state" refers to data that indicates an individual's emotional condition, and includes emotional indicators such as stress levels and mood swings.

[0158] "Profiling" is the process of analyzing collected information to reveal individual characteristics and tendencies.

[0159] "Nutritional balance" refers to a state in which a meal contains all the nutrients the body needs in the appropriate proportions.

[0160] An "artificial intelligence model" is a computer program that analyzes data and performs predictions and classifications, and is a system that evolves based on learning algorithms.

[0161] "Feedback" refers to the reactions and opinions provided by users, and the system is improved and adjusted based on this feedback.

[0162] A specific description will be given of embodiments for carrying out this invention.

[0163] This system is designed to provide personalized meal suggestions to individual users. Users can input data on their meal history, health status, and emotional state using a device with a dedicated application installed. Emotional state is collected in real time by analyzing the user's voice and facial expressions. Smartphones and wearable devices are used as hardware for this process.

[0164] Data collected on the device is securely transmitted to the server. The server uses AI frameworks such as TensorFlow as data analysis software to analyze the collected information. As a result of the analysis, a user-specific nutrition profile is generated, which supports the selection of nutritional balance and ingredients that are appropriate for each individual user.

[0165] Furthermore, the server uses a generative AI model to generate customized meal menus from the nutritional profile. These generated menus are presented to the user via a terminal. The menus include detailed nutritional information, cooking instructions, and information on purchasing related products.

[0166] Users select and prepare their meals based on the provided menu. After the meal, users send feedback to the server through the application. The server updates its AI model based on the received feedback, improving the accuracy of future suggestions. This enables meal suggestions optimized for each user.

[0167] For example, by entering a prompt message into the server such as, "Please suggest a suitable meal menu when the user is feeling stressed," an appropriate menu will be generated. In this way, the system effectively functions to enhance satisfaction and emotional fulfillment related to food.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The device collects data from the user. The user inputs their meal history, health status, and emotional state through the application. Emotional states are captured in real time using voice and facial expression analysis technology. The input data is converted to an appropriate format, such as JSON, on the device.

[0171] Step 2:

[0172] The device sends the collected data to the server. The data is encrypted using the HTTPS protocol to ensure security during transfer to the cloud server. This data transfer allows the server to receive detailed user information.

[0173] Step 3:

[0174] The server analyzes the data it receives. Based on this data, an AI algorithm is used to profile the individual's nutritional status, food preferences, and emotional state. Specifically, a machine learning model runs on the TensorFlow framework to learn the user's eating and emotional patterns. As a result of the analysis, a user-specific nutritional profile is generated.

[0175] Step 4:

[0176] The server uses an AI model to create personalized meal plans. Based on the generated nutritional profile, it constructs menus that are nutritionally balanced and considerate of emotional needs. In this process, appropriate ingredients and recipes are selected from the database, and the proposed meal plan is completed.

[0177] Step 5:

[0178] The server generates a menu and sends it to the terminal. The terminal then presents it to the user. The menu displayed in the user interface includes detailed nutritional information for the dishes, cooking instructions, and a list of available products. The user can then choose and prepare their actual meal based on this information.

[0179] Step 6:

[0180] The system collects user feedback on their meal experience via a device. Users input their thoughts and satisfaction level after the meal using the application. The entered feedback data is then sent back to the server from the device.

[0181] Step 7:

[0182] The server updates the AI ​​model based on the feedback it receives. The machine learning model is then readjusted based on the new feedback data to improve prediction accuracy. This will result in future meal recommendations being more tailored to the user.

[0183] (Application Example 2)

[0184] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0185] Conventional meal suggestion systems only consider an individual's nutritional status and food preferences, failing to reflect their emotional state and making it difficult to support a satisfying diet for each user. Furthermore, they lack concrete means for users to determine whether the suggested menu is actually available for purchase or use, resulting in a lot of hassle for users before they can implement the suggestions.

[0186] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0187] In this invention, the server includes means for collecting an individual's meal history, health data, and emotional information; means for analyzing the collected data to profile the individual's nutritional status, food preferences, and emotional state; and means for generating a meal menu that matches the nutritional balance, personal preferences, and emotional state based on the profiling results, and further automatically issuing delivery instructions according to the meal menu. This enables personalized meal suggestions that are tailored to the emotional needs of each user and supports the immediate execution of the suggested menu.

[0188] A "dietary history" is a record of the foods an individual has consumed in the past, and is fundamental information for analyzing nutritional intake patterns and food preferences.

[0189] "Health data" refers to data that indicates an individual's physical health status, and includes information such as medical records and self-reported health indicators.

[0190] "Emotional information" refers to data that indicates an individual's psychological state and emotional changes, and is collected through voice analysis and facial expression analysis.

[0191] "Profiling" is the process of analyzing an individual's characteristics based on collected data, revealing traits such as nutritional status, food preferences, and emotional state.

[0192] A "meal menu" refers to the specific contents of a meal proposed to an individual, taking into consideration nutritional balance and ingredients and cooking methods that address their emotional state.

[0193] "Feedback" refers to users recording their satisfaction levels and opinions on suggested meal menus, providing valuable information to improve the accuracy of future suggestions.

[0194] A "delivery instruction" is a command that automatically handles the purchase and delivery procedures based on the suggested meal menu, and is intended to quickly provide ingredients and prepared meals to the user.

[0195] This system collects personal data via the user's smart device, analyzes it, and then provides meal suggestions. Specifically, devices such as smartphones and smart glasses collect the user's meal history, health data, and emotional information in real time. This emotional information is obtained through voice and facial expression analysis. The device then transmits this data to a server.

[0196] The server analyzes collected data using AI algorithms to profile the user's nutritional status, food preferences, and emotional state. This utilizes the Google® Cloud Speech-to-Text API for speech analysis and OpenCV for image analysis. Based on the profiling results, the server generates meal menus tailored to the user's nutritional balance and emotional state. TensorFlow is used as the AI ​​model for creating user profiles and optimizing meal suggestions.

[0197] Furthermore, if the proposed menu is approved, the server uses an API to automate delivery instructions and connects with a food delivery service. This ensures that ingredients and meals are delivered to the user at the appropriate time.

[0198] This process enables comprehensive meal suggestions tailored to the individual user's emotional needs. For example, if a user is detected to be experiencing mild stress in the afternoon, the system will suggest relaxing beverages and snacks and arrange for them to be delivered immediately from a nearby cafe.

[0199] An example of a prompt message is: "Design a system that assesses the user's emotional state and suggests food and drink menus to reduce stress. Input data will include the user's voice and facial expression images." The aim is to improve user satisfaction by adding emotional adaptability to food selection.

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The device receives input from the user. This input includes meal history, health data, and emotional information collected through voice and facial expressions. The input data is preprocessed for sentiment analysis, and voice data is converted to text by the Google Cloud Speech-to-Text API.

[0203] Step 2:

[0204] The terminal sends pre-processed data to the server. Input data, including text data converted from speech, health data, and image data for sentiment analysis, is delivered to the server, which then uses this data to perform analysis.

[0205] Step 3:

[0206] The server analyzes the received data using an AI algorithm to generate a profile of the user's nutritional status, food preferences, and emotional state. Here, TensorFlow is used to create a unique profile from various data and extract specific features corresponding to the user's health and emotions.

[0207] Step 4:

[0208] The server creates meal menus based on the generated profiles. The menus are designed using ingredients and cooking methods that take into account nutritional balance and emotional state. This stage includes emotional support, such as suggesting relaxing ingredients when stress levels are high.

[0209] Step 5:

[0210] The server returns the created menu to the terminal and presents it to the user. The user reviews the menu and, upon selection, the order is executed. The user's selection is registered as input, and the meal menu to be served is determined as a result.

[0211] Step 6:

[0212] After receiving user approval, the server automatically sends delivery instructions to the food delivery service. Using an API, the delivery process is automated based on available product information, and ingredients and meals are prepared to be delivered to the user.

[0213] Step 7:

[0214] After receiving their meal, users input feedback into the system. The collected feedback is sent to the system, and the server uses it to generate the next menu. Based on the collected feedback, the AI ​​model is updated, improving the accuracy of future suggestions.

[0215] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0216] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0217] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0221] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0222] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0223] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0224] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0225] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0226] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0227] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0228] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0229] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0230] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0231] This invention is a system that provides meal suggestions that take into account an individual's nutritional status and preferences. The specific operation of the system is described below.

[0232] First, the terminal serves as a receiver for user input. Users input their eating history, health checkup results, allergy information, and other data into the system via the terminal. This information becomes the basic data for analyzing an individual's nutritional status and food preferences.

[0233] Next, this input data is sent to the server. Based on the received data, the server uses an AI algorithm to create an individual nutrition profile. The nutrition profile includes the user's past eating habits, nutrient deficiencies or excesses, and allergy risks.

[0234] The AI ​​model automatically generates personalized meal plans based on this nutritional profile. The generated menus consider a healthy nutritional balance and are tailored to the user's preferences and allergy information.

[0235] Next, the generated menu is sent to the terminal and presented to the user. The user can then procure ingredients and cook according to the presented menu. The menu also includes nutritional information and information on potential convenience store items to support smooth ingredient procurement.

[0236] After a user tries a menu item, they input their results and impressions as feedback. This feedback is sent back to the server, and the system uses this information to adjust the AI ​​algorithm. This is to enable the system to suggest more personalized content for future menu generation.

[0237] As a concrete example, when user A uses the system, the server analyzes the user's dietary history to determine that they are lacking protein and suggests a "healthy bowl using tofu." Furthermore, depending on whether or not the user has a dairy allergy, alternative dairy-free products are added to their shopping list at the convenience store.

[0238] In this way, the system suggests meals best suited to individual circumstances, helping users easily maintain a healthy diet. This invention provides a groundbreaking means of conveniently realizing a healthy diet for people.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] Users log in to their devices and enter their meal history, health checkup results, and allergy information. This entered data is linked to the user's individual account.

[0242] Step 2:

[0243] The terminal sends the entered data to the system's server. The data is encrypted and transmitted securely.

[0244] Step 3:

[0245] The server stores the received data in a database and analyzes it using AI algorithms. Specifically, it profiles the user's nutritional status and food preferences to identify nutrient deficiencies and excesses.

[0246] Step 4:

[0247] Based on the analysis results, the server generates a nutritionally balanced meal menu suitable for the user. The algorithm constructs the menu considering the nutritional value of ingredients, the user's preferences, and allergy information.

[0248] Step 5:

[0249] The server sends the generated menu to the terminal. The transmitted data includes nutritional information and a list of related products that can be purchased at convenience stores.

[0250] Step 6:

[0251] The terminal displays the received menu information to the user. The user reviews the displayed menu, purchases ingredients as needed, and prepares the meal.

[0252] Step 7:

[0253] After the user tries the suggested menu items, they input feedback about the results and their satisfaction level into the device.

[0254] Step 8:

[0255] The device sends user feedback to the server. Based on this feedback, the server updates the AI ​​model to improve accuracy in future menu generation.

[0256] (Example 1)

[0257] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0258] In modern society, it is extremely difficult to propose nutritionally balanced meals tailored to individual health conditions and preferences. Moreover, for people leading busy lives, it is difficult to easily obtain information on how to select and purchase appropriate ingredients. As a result, there is a growing problem of an increasing number of people not getting enough nutrients necessary for maintaining good health. Furthermore, those with allergies face the risk of accidentally consuming dangerous ingredients. Therefore, there is a need for a system that can automatically generate appropriate meal menus, taking into account an individual's nutritional status and food preferences, and provide the necessary information.

[0259] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0260] In this invention, the server includes means for acquiring meal history and health information, means for analyzing the acquired data to design a nutritional profile, and means for generating meal menus using a generation AI model based on the nutritional profile. This enables the automatic provision of nutritionally balanced meal menus tailored to individual needs, thereby realizing a healthy lifestyle.

[0261] "Individual" refers to a specific person, and the subject of creating a nutritional profile based on that person's dietary history and health information.

[0262] A "nutritional profile" is a dataset that comprehensively includes information on nutrient deficiencies and excesses, food preferences, and allergies, analyzed using an individual's dietary history and health information.

[0263] A "generative AI model" refers to an algorithm that automatically generates meal menus that are balanced nutritionally and tailored to individual preferences, based on an individual's nutritional profile.

[0264] A "meal menu" refers to a personalized list of specific foods and dishes created by a generative AI model, taking into account nutritional value and personal preferences.

[0265] "Responses" refer to the opinions and evaluations that individuals give regarding the presented meal menu, and this information is collected to help create future menus.

[0266] "Product information" refers to detailed data about purchasable products related to meal menus, and is information that can be used as a reference when individuals procure appropriate ingredients.

[0267] This invention is a system that proposes customized meal menus based on an individual's nutritional status and food preferences. Specific embodiments of the system are described below.

[0268] The terminal functions as a device for receiving data input from users. Users input their meal history, health checkup data, and allergy information through an interface on the terminal. This interface is implemented using a dedicated application or web-based form and is designed to be easy for users to use. The data entered from the terminal is converted into a structured data format, such as JSON format, and sent to the server using a secure protocol.

[0269] The server processes the received data and performs analysis through AI algorithms. Specifically, the AI ​​model is developed in Python and utilizes libraries such as TensorFlow or PyTorch. This model generates a nutritional profile and creates menus that reflect the user's eating habits, nutrient deficiencies or excesses, and allergy information. At this time, the server accesses a database to refer to the latest nutritional data and ingredient information.

[0270] The generated menus are tailored to nutritional balance and individual preferences, and also include information on available products. For example, a user lacking protein might be suggested a "nutritious dish using tofu," and alternative ingredients would be suggested based on allergy information. As a concrete example, a prompt used in the generating AI model might be something like, "Generate a recommended meal plan for the next week based on the user's eating history and health data."

[0271] Finally, the generated menu is sent back to the terminal. The user can immediately view the menu and efficiently procure the necessary ingredients by referring to the purchasing information associated with the menu.

[0272] In this way, the system provides meal suggestions tailored to specific users, supporting them in achieving a healthy diet.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The terminal receives input data from the user. Here, the user enters their meal history, health status information, and allergy information via an application or web interface. This input data is processed into JSON format. The final output is structured data that is securely sent to the server.

[0276] Step 2:

[0277] The server receives data sent from the terminal. The server analyzes the received JSON data and starts creating a nutrition profile by matching it against a database. Based on the input data, it calculates nutrient deficiencies and excesses as well as allergy risks. The output of this analysis is a detailed dataset that serves as the individual's nutrition profile.

[0278] Step 3:

[0279] The server's AI algorithm receives the nutrition profile as input using a generated AI model and generates a meal menu suitable for the user. In this process, menu generation is performed using a prompt sentence. For example, the prompt sentence "Create a one-week healthy meal menu based on the nutritional status of User A" is used. The output is a meal menu plan that harmonizes with nutritional value and the individual's preferences.

[0280] Step 4:

[0281] The server sends the generated menu to the terminal in a structured data format. The terminal analyzes the received data and displays it in an interface that is easy for the user to view. As output, the user can view a detailed meal menu and related purchase information.

[0282] Step 5:

[0283] After the user practices the presented meal menu, feedback is input from the terminal into the system. The feedback input includes feelings and improvement points about the actually cooked dishes. The terminal processes this information back into JSON format and sends it to the server.

[0284] Step 6:

[0285] The server analyzes the feedback sent by the user and reflects it in the AI algorithm to improve the accuracy of the system. Based on the input feedback, adjustments are made for utilization in the next menu proposal. This output is adjustment data including improvement plans for the next proposal.

[0286] (Application Example 1)

[0287] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] In the modern busy living environment, it is difficult for many people to select and prepare appropriate meals based on their individual health conditions and nutritional balances. As a result, health problems such as overnutrition, undernutrition, and allergic reactions are likely to occur. Also, it is not easy to find meals that suit an individual's preferences. There is a need to solve these problems and provide healthy and personalized meals.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0290] In this invention, the server includes means for collecting an individual's meal history and health data, means for analyzing the collected data to profile the individual's nutritional status and food preferences, and means for generating a meal menu that matches the nutritional balance and individual preferences based on the profiling results. This enables the selection and provision of meals according to individual needs.

[0291] The "meal history" is information that records the content of meals an individual has consumed in the past, the number of times, and the time, etc.

[0292] The "health data" is information representing an individual's health status and includes the results of health check-ups and daily health management data.

[0293] "Profiling" refers to the process of analyzing collected data to understand an individual's preferences and tendencies, and then creating a model based on that understanding.

[0294] "Nutritional balance" refers to a state in which the human body is consuming the necessary nutrients in appropriate proportions.

[0295] A "meal menu" is a list that shows the contents and menu of a meal, and is structured according to a specific purpose or condition.

[0296] "Meal service" refers to the business or activity of providing meals and ingredients, and arranging food according to the needs of the users.

[0297] "Communication methods" refer to the technical mechanisms for exchanging data and information between various devices and systems.

[0298] This invention is a system that supports a healthy diet tailored to each individual. The server receives meal history and health data transmitted by the user and uses an AI algorithm to generate an individual nutritional profile based on this data. This AI model takes into particular consideration nutritional balance, individual food preferences, and allergy information. Based on the generated profile, the server creates a meal menu optimized for the individual's needs and transmits the menu information to the user's terminal via communication means.

[0299] The user's device uses a cross-platform mobile application built with React Native to browse menus and order meals in conjunction with the food service options offered. The backend system is built with Node.js and Express to manage user data and information from service providers. A MongoDB database enables the continuous accumulation of individual nutritional profiles and menu information.

[0300] As a specific example, when a user inputs their meal history and health examination results through an application, the server analyzes the vitamin D deficiency and proposes "grilled salmon and spinach". Then, the dish can be ordered through a partnered restaurant via a delivery service and delivered to the user.

[0301] Examples of prompt texts for the generative AI model include content such as "Analyze the nutrients lacking from the user's meal history and show a recommended plan when vitamin D is deficient. However, the user has a crustacean allergy."

[0302] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0303] Step 1:

[0304] The user uses a terminal to input their meal history and health data.

[0305] The input data includes past meal content, health examination results, allergy information, etc. The terminal sends this data to the server. As output, the user's individual data is accumulated on the server.

[0306] Step 2:

[0307] The server analyzes the received data to profile the individual's nutritional status and food preferences.

[0308] The input is the data sent from the user, and the output is the individual's nutritional profile. The server uses an AI algorithm to perform data analysis and profiling. The generative AI model is utilized in this process.

[0309] Step 3:

[0310] Based on the profiling results, the server generates a meal menu considering nutritional balance.

[0311] The input is a nutritional profile, and the output is a meal menu. The server inputs prompts into a generative model and automatically generates the menu.

[0312] Step 4:

[0313] The server sends the generated menu to the terminal.

[0314] As output, a personalized meal menu is displayed on the user's device. The user can select their preferred items from these menus.

[0315] Step 5:

[0316] Users select menu items via their terminal and place orders through the corresponding food service.

[0317] The input is the user's menu selection, and the output is the transmission of the selection information to the food service. The selected menu item is then ordered through integration with the service provider's system.

[0318] Step 6:

[0319] Users actually receive the food and provide feedback.

[0320] The input is feedback based on the experience of the provided dish, and the output is the transmission of this new feedback data to the server. This feedback will be used in the next menu generation process.

[0321] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0322] This invention relates to a system that provides personalized meal suggestions, taking into account a user's eating history, health data, and emotional state. The system aims to provide menus that satisfy not only the user's nutritional needs but also their emotional needs.

[0323] First, the device receives input from the user. At this time, the user inputs not only their meal history and health data, but also their emotional state in real time through an emotion engine. This emotional information is collected through voice and facial expression analysis.

[0324] The received data is sent to a server. The server analyzes the data using an AI algorithm and creates an individualized nutritional profile. This profile combines the user's daily eating habits, health status, and even emotional data.

[0325] Next, the server's algorithm generates a meal menu tailored to the user. This process considers not only nutritional balance but also ingredients and cooking methods that address the user's emotional state. For example, it might suggest a relaxing herbal tea when the user is under high stress.

[0326] The generated menu is then presented to the user again via the terminal. The menu includes detailed information such as necessary nutritional information, a list of items that can be purchased at convenience stores, and the effects of ingredients that correspond to emotions.

[0327] After users actually try the menu, they input their results and impressions into the system as feedback. This feedback is based on changes in the user's emotions and their satisfaction with the meal.

[0328] Finally, the server updates the AI ​​model based on the feedback received, improving the accuracy of future menu suggestions. In this way, the system is optimized for each user, supporting a highly satisfying eating lifestyle not only nutritionally but also emotionally.

[0329] As a concrete example, when user B logs in, it is discovered that their mood is unstable that day. As a result, the server suggests oatmeal, which has a calming effect, and a fruit salad containing vitamin C, which can boost mood. In this way, a comprehensive meal recommendation that takes emotional state into consideration is realized.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] The device accepts input from the user. The user inputs their meal history, health data, and emotional information. The emotional information is processed in real time by an emotion engine, which analyzes voice and facial expressions via microphones and cameras to identify emotions.

[0333] Step 2:

[0334] The terminal encrypts all entered data before sending it to the server for security purposes. This ensures that data transfer is performed while protecting user privacy.

[0335] Step 3:

[0336] The server uses AI algorithms to analyze the received data. Based on meal history and health data, nutritional status is analyzed, and at the same time, an emotion engine evaluates the user's emotional state, creating an integrated nutritional profile.

[0337] Step 4:

[0338] The server generates meal plans based on the generated nutritional profile. It proposes individually optimized menus that incorporate nutritional balance, preferred ingredients, and specific foods and ingredients that suit the user's emotional state.

[0339] Step 5:

[0340] The server sends the created menu back to the terminal. The terminal provides the user with menu details, nutritional information, and reasoning behind ingredient selections based on emotions. A list of items available at convenience stores is also attached.

[0341] Step 6:

[0342] Users eat according to the provided meal menu and observe changes in their emotions and level of satisfaction. After finishing the meal, users input feedback about their satisfaction and emotional changes into the device.

[0343] Step 7:

[0344] The device securely sends feedback to the server. The server uses this feedback to update the AI ​​algorithm model and adapt it to further improve accuracy in future menu generation.

[0345] Through this series of steps, the system becomes capable of providing more personalized menus tailored to each user's unique nutritional status and emotional state.

[0346] (Example 2)

[0347] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0348] When suggesting meals, it is desirable to consider not only individual health conditions and food preferences but also emotional states. However, conventional systems have the challenge of making personalized suggestions that reflect emotions. Furthermore, in order to improve the accuracy and satisfaction of meal suggestions, a system that efficiently utilizes individual feedback is required.

[0349] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0350] In this invention, the server includes means for collecting an individual's eating history, health status, and emotional state; means for analyzing the collected data to profile the individual's nutritional status, food preferences, and emotional state; and means for updating an artificial intelligence model based on the individual's feedback. This enables a high level of personalization that takes into account the individual's health status, food preferences, and emotional trends, thereby increasing user satisfaction.

[0351] A "dietary history" is a collection of information about the types, times, and frequency of meals an individual has eaten in the past.

[0352] "Health status" refers to information about an individual's physical health, including medical history, allergy information, and anthropometric data.

[0353] "Emotional state" refers to data that indicates an individual's emotional condition, and includes emotional indicators such as stress levels and mood swings.

[0354] "Profiling" is the process of analyzing collected information to reveal individual characteristics and tendencies.

[0355] "Nutritional balance" refers to a state in which a meal contains all the nutrients the body needs in the appropriate proportions.

[0356] An "artificial intelligence model" is a computer program that analyzes data and performs predictions and classifications, and is a system that evolves based on learning algorithms.

[0357] "Feedback" refers to the reactions and opinions provided by users, and the system is improved and adjusted based on this feedback.

[0358] A specific description will be given of embodiments for carrying out this invention.

[0359] This system is designed to provide personalized meal suggestions to individual users. Users can input data on their meal history, health status, and emotional state using a device with a dedicated application installed. Emotional state is collected in real time by analyzing the user's voice and facial expressions. Smartphones and wearable devices are used as hardware for this process.

[0360] Data collected on the device is securely transmitted to the server. The server uses AI frameworks such as TensorFlow as data analysis software to analyze the collected information. As a result of the analysis, a user-specific nutrition profile is generated, which supports the selection of nutritional balance and ingredients that are appropriate for each individual user.

[0361] Furthermore, the server uses a generative AI model to generate customized meal menus from the nutritional profile. These generated menus are presented to the user via a terminal. The menus include detailed nutritional information, cooking instructions, and information on purchasing related products.

[0362] Users select and prepare their meals based on the provided menu. After the meal, users send feedback to the server through the application. The server updates its AI model based on the received feedback, improving the accuracy of future suggestions. This enables meal suggestions optimized for each user.

[0363] For example, by entering a prompt message into the server such as, "Please suggest a suitable meal menu when the user is feeling stressed," an appropriate menu will be generated. In this way, the system effectively functions to enhance satisfaction and emotional fulfillment related to food.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] The device collects data from the user. The user inputs their meal history, health status, and emotional state through the application. Emotional states are captured in real time using voice and facial expression analysis technology. The input data is converted to an appropriate format, such as JSON, on the device.

[0367] Step 2:

[0368] The device sends the collected data to the server. The data is encrypted using the HTTPS protocol to ensure security during transfer to the cloud server. This data transfer allows the server to receive detailed user information.

[0369] Step 3:

[0370] The server analyzes the data it receives. Based on this data, an AI algorithm is used to profile the individual's nutritional status, food preferences, and emotional state. Specifically, a machine learning model runs on the TensorFlow framework to learn the user's eating and emotional patterns. As a result of the analysis, a user-specific nutritional profile is generated.

[0371] Step 4:

[0372] The server uses an AI model to create personalized meal plans. Based on the generated nutritional profile, it constructs menus that are nutritionally balanced and considerate of emotional needs. In this process, appropriate ingredients and recipes are selected from the database, and the proposed meal plan is completed.

[0373] Step 5:

[0374] The server generates a menu and sends it to the terminal. The terminal then presents it to the user. The menu displayed in the user interface includes detailed nutritional information for the dishes, cooking instructions, and a list of available products. The user can then choose and prepare their actual meal based on this information.

[0375] Step 6:

[0376] The system collects user feedback on their meal experience via a device. Users input their thoughts and satisfaction level after the meal using the application. The entered feedback data is then sent back to the server from the device.

[0377] Step 7:

[0378] The server updates the AI ​​model based on the feedback it receives. The machine learning model is then readjusted based on the new feedback data to improve prediction accuracy. This will result in future meal recommendations being more tailored to the user.

[0379] (Application Example 2)

[0380] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0381] Conventional meal suggestion systems only consider an individual's nutritional status and food preferences, failing to reflect their emotional state and making it difficult to support a satisfying diet for each user. Furthermore, they lack concrete means for users to determine whether the suggested menu is actually available for purchase or use, resulting in a lot of hassle for users before they can implement the suggestions.

[0382] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0383] In this invention, the server includes means for collecting an individual's meal history, health data, and emotional information; means for analyzing the collected data to profile the individual's nutritional status, food preferences, and emotional state; and means for generating a meal menu that matches the nutritional balance, personal preferences, and emotional state based on the profiling results, and further automatically issuing delivery instructions according to the meal menu. This enables personalized meal suggestions that are tailored to the emotional needs of each user and supports the immediate execution of the suggested menu.

[0384] A "dietary history" is a record of the foods an individual has consumed in the past, and is fundamental information for analyzing nutritional intake patterns and food preferences.

[0385] "Health data" refers to data that indicates an individual's physical health status, and includes information such as medical records and self-reported health indicators.

[0386] "Emotional information" refers to data that indicates an individual's psychological state and emotional changes, and is collected through voice analysis and facial expression analysis.

[0387] "Profiling" is the process of analyzing an individual's characteristics based on collected data, revealing traits such as nutritional status, food preferences, and emotional state.

[0388] A "meal menu" refers to the specific contents of a meal proposed to an individual, taking into consideration nutritional balance and ingredients and cooking methods that address their emotional state.

[0389] "Feedback" refers to users recording their satisfaction levels and opinions on suggested meal menus, providing valuable information to improve the accuracy of future suggestions.

[0390] A "delivery instruction" is a command that automatically handles the purchase and delivery procedures based on the suggested meal menu, and is intended to quickly provide ingredients and prepared meals to the user.

[0391] This system collects personal data via the user's smart device, analyzes it, and then provides meal suggestions. Specifically, devices such as smartphones and smart glasses collect the user's meal history, health data, and emotional information in real time. This emotional information is obtained through voice and facial expression analysis. The device then transmits this data to a server.

[0392] The server analyzes collected data using AI algorithms to profile the user's nutritional status, food preferences, and emotional state. This utilizes the Google Cloud Speech-to-Text API for speech analysis and OpenCV for image analysis. Based on the profiling results, the server generates meal menus tailored to the user's nutritional balance and emotional state. TensorFlow is used as the AI ​​model for creating user profiles and optimizing meal suggestions.

[0393] Furthermore, if the proposed menu is approved, the server uses an API to automate delivery instructions and connects with a food delivery service. This ensures that ingredients and meals are delivered to the user at the appropriate time.

[0394] This process enables comprehensive meal suggestions tailored to the individual user's emotional needs. For example, if a user is detected to be experiencing mild stress in the afternoon, the system will suggest relaxing beverages and snacks and arrange for them to be delivered immediately from a nearby cafe.

[0395] An example of a prompt message is: "Design a system that assesses the user's emotional state and suggests food and drink menus to reduce stress. Input data will include the user's voice and facial expression images." The aim is to improve user satisfaction by adding emotional adaptability to food selection.

[0396] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0397] Step 1:

[0398] The device receives input from the user. This input includes meal history, health data, and emotional information collected through voice and facial expressions. The input data is preprocessed for sentiment analysis, and voice data is converted to text by the Google Cloud Speech-to-Text API.

[0399] Step 2:

[0400] The terminal sends pre-processed data to the server. Input data, including text data converted from speech, health data, and image data for sentiment analysis, is delivered to the server, which then uses this data to perform analysis.

[0401] Step 3:

[0402] The server analyzes the received data using an AI algorithm to generate a profile of the user's nutritional status, food preferences, and emotional state. Here, TensorFlow is used to create a unique profile from various data and extract specific features corresponding to the user's health and emotions.

[0403] Step 4:

[0404] The server creates meal menus based on the generated profiles. The menus are designed using ingredients and cooking methods that take into account nutritional balance and emotional state. This stage includes emotional support, such as suggesting relaxing ingredients when stress levels are high.

[0405] Step 5:

[0406] The server returns the created menu to the terminal and presents it to the user. The user reviews the menu and, upon selection, the order is executed. The user's selection is registered as input, and the meal menu to be served is determined as a result.

[0407] Step 6:

[0408] After receiving user approval, the server automatically sends delivery instructions to the food delivery service. Using an API, the delivery process is automated based on available product information, and ingredients and meals are prepared to be delivered to the user.

[0409] Step 7:

[0410] After receiving their meal, users input feedback into the system. The collected feedback is sent to the system, and the server uses it to generate the next menu. Based on the collected feedback, the AI ​​model is updated, improving the accuracy of future suggestions.

[0411] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0412] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0413] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0414] [Third Embodiment]

[0415] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0416] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0417] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0418] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0419] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0420] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0421] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0422] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0423] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0424] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0425] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0426] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0427] This invention is a system that provides meal suggestions that take into account an individual's nutritional status and preferences. The specific operation of the system is described below.

[0428] First, the terminal serves as a receiver for user input. Users input their eating history, health checkup results, allergy information, and other data into the system via the terminal. This information becomes the basic data for analyzing an individual's nutritional status and food preferences.

[0429] Next, this input data is sent to the server. Based on the received data, the server uses an AI algorithm to create an individual nutrition profile. The nutrition profile includes the user's past eating habits, nutrient deficiencies or excesses, and allergy risks.

[0430] The AI ​​model automatically generates personalized meal plans based on this nutritional profile. The generated menus consider a healthy nutritional balance and are tailored to the user's preferences and allergy information.

[0431] Next, the generated menu is sent to the terminal and presented to the user. The user can then procure ingredients and cook according to the presented menu. The menu also includes nutritional information and information on potential convenience store items to support smooth ingredient procurement.

[0432] After a user tries a menu item, they input their results and impressions as feedback. This feedback is sent back to the server, and the system uses this information to adjust the AI ​​algorithm. This is to enable the system to suggest more personalized content for future menu generation.

[0433] As a concrete example, when user A uses the system, the server analyzes the user's dietary history to determine that they are lacking protein and suggests a "healthy bowl using tofu." Furthermore, depending on whether or not the user has a dairy allergy, alternative dairy-free products are added to their shopping list at the convenience store.

[0434] In this way, the system suggests meals best suited to individual circumstances, helping users easily maintain a healthy diet. This invention provides a groundbreaking means of conveniently realizing a healthy diet for people.

[0435] The following describes the processing flow.

[0436] Step 1:

[0437] Users log in to their devices and enter their meal history, health checkup results, and allergy information. This entered data is linked to the user's individual account.

[0438] Step 2:

[0439] The terminal sends the entered data to the system's server. The data is encrypted and transmitted securely.

[0440] Step 3:

[0441] The server stores the received data in a database and analyzes it using AI algorithms. Specifically, it profiles the user's nutritional status and food preferences to identify nutrient deficiencies and excesses.

[0442] Step 4:

[0443] Based on the analysis results, the server generates a nutritionally balanced meal menu suitable for the user. The algorithm constructs the menu considering the nutritional value of ingredients, the user's preferences, and allergy information.

[0444] Step 5:

[0445] The server sends the generated menu to the terminal. The transmitted data includes nutritional information and a list of related products that can be purchased at convenience stores.

[0446] Step 6:

[0447] The terminal displays the received menu information to the user. The user reviews the displayed menu, purchases ingredients as needed, and prepares the meal.

[0448] Step 7:

[0449] After the user tries the suggested menu items, they input feedback about the results and their satisfaction level into the device.

[0450] Step 8:

[0451] The device sends user feedback to the server. Based on this feedback, the server updates the AI ​​model to improve accuracy in future menu generation.

[0452] (Example 1)

[0453] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0454] In modern society, it is extremely difficult to propose nutritionally balanced meals tailored to individual health conditions and preferences. Moreover, for people leading busy lives, it is difficult to easily obtain information on how to select and purchase appropriate ingredients. As a result, there is a growing problem of an increasing number of people not getting enough nutrients necessary for maintaining good health. Furthermore, those with allergies face the risk of accidentally consuming dangerous ingredients. Therefore, there is a need for a system that can automatically generate appropriate meal menus, taking into account an individual's nutritional status and food preferences, and provide the necessary information.

[0455] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0456] In this invention, the server includes means for acquiring meal history and health information, means for analyzing the acquired data to design a nutritional profile, and means for generating meal menus using a generation AI model based on the nutritional profile. This enables the automatic provision of nutritionally balanced meal menus tailored to individual needs, thereby realizing a healthy lifestyle.

[0457] "Individual" refers to a specific person, and the subject of creating a nutritional profile based on that person's dietary history and health information.

[0458] A "nutritional profile" is a dataset that comprehensively includes information on nutrient deficiencies and excesses, food preferences, and allergies, analyzed using an individual's dietary history and health information.

[0459] A "generative AI model" refers to an algorithm that automatically generates meal menus that are balanced nutritionally and tailored to individual preferences, based on an individual's nutritional profile.

[0460] A "meal menu" refers to a personalized list of specific foods and dishes created by a generative AI model, taking into account nutritional value and personal preferences.

[0461] "Responses" refer to the opinions and evaluations that individuals give regarding the presented meal menu, and this information is collected to help create future menus.

[0462] "Product information" refers to detailed data about purchasable products related to meal menus, and is information that can be used as a reference when individuals procure appropriate ingredients.

[0463] This invention is a system that proposes customized meal menus based on an individual's nutritional status and food preferences. Specific embodiments of the system are described below.

[0464] The terminal functions as a device for receiving data input from users. Users input their meal history, health checkup data, and allergy information through an interface on the terminal. This interface is implemented using a dedicated application or web-based form and is designed to be easy for users to use. The data entered from the terminal is converted into a structured data format, such as JSON format, and sent to the server using a secure protocol.

[0465] The server processes the received data and performs analysis through AI algorithms. Specifically, the AI ​​model is developed in Python and utilizes libraries such as TensorFlow or PyTorch. This model generates a nutritional profile and creates menus that reflect the user's eating habits, nutrient deficiencies or excesses, and allergy information. At this time, the server accesses a database to refer to the latest nutritional data and ingredient information.

[0466] The generated menus are tailored to nutritional balance and individual preferences, and also include information on available products. For example, a user lacking protein might be suggested a "nutritious dish using tofu," and alternative ingredients would be suggested based on allergy information. As a concrete example, a prompt used in the generating AI model might be something like, "Generate a recommended meal plan for the next week based on the user's eating history and health data."

[0467] Finally, the generated menu is sent back to the terminal. The user can immediately view the menu and efficiently procure the necessary ingredients by referring to the purchasing information associated with the menu.

[0468] In this way, the system provides meal suggestions tailored to specific users, supporting them in achieving a healthy diet.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] The terminal receives input data from the user. Here, the user enters their meal history, health status information, and allergy information via an application or web interface. This input data is processed into JSON format. The final output is structured data that is securely sent to the server.

[0472] Step 2:

[0473] The server receives data sent from the terminal. The server parses the received JSON data and starts creating a nutritional profile by comparing it with the database. Based on the input data, it calculates nutrient deficiencies or excesses and allergy risks. The output of this analysis is a detailed dataset that constitutes the individual's nutritional profile.

[0474] Step 3:

[0475] The server's AI algorithm uses a generated AI model to receive a nutritional profile as input and generate a meal plan suitable for the user. This process uses prompts to generate the menu. For example, the prompt might be "Create a healthy one-week menu based on user A's nutritional status." The output is a meal plan that balances nutritional value with personal preferences.

[0476] Step 4:

[0477] The server sends the generated menu to the terminal in structured data format. The terminal parses the received data and displays it in a user-friendly interface. As output, the user can view a detailed meal menu and related purchase information.

[0478] Step 5:

[0479] After the user prepares the meal according to the provided menu, they input feedback into the system via their device. This feedback includes their impressions of the dish they actually prepared and suggestions for improvement. The device then processes this information back into JSON format and sends it to the server.

[0480] Step 6:

[0481] The server analyzes user feedback and incorporates it into the AI ​​algorithm to improve the system's accuracy. Based on the input feedback, adjustments are made to inform the next menu suggestion. This output is adjustment data, including suggestions for improvement for the next suggestion.

[0482] (Application Example 1)

[0483] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0484] In today's busy lifestyle, selecting and preparing appropriate meals based on individual health conditions and nutritional balance is difficult for many people. As a result, health problems such as nutritional excess, deficiency, and allergic reactions are common. Furthermore, finding meals that suit individual preferences is not easy. There is a need to solve these problems and provide healthy meals that are tailored to each individual.

[0485] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0486] In this invention, the server includes means for collecting an individual's dietary history and health data, means for analyzing the collected data to profile the individual's nutritional status and food preferences, and means for generating a meal menu that matches nutritional balance and individual preferences based on the profiling results. This makes it possible to select and provide meals that meet individual needs.

[0487] "Dietary history" refers to information that records the content, frequency, and timing of meals an individual has consumed in the past.

[0488] "Health data" refers to information that represents an individual's health status, and includes results from health checkups and daily health management data.

[0489] "Profiling" refers to the process of analyzing collected data to understand an individual's preferences and tendencies, and then creating a model based on that understanding.

[0490] "Nutritional balance" refers to a state in which the human body is consuming the necessary nutrients in appropriate proportions.

[0491] A "meal menu" is a list that shows the contents and menu of a meal, and is structured according to a specific purpose or condition.

[0492] "Meal service" refers to the business or activity of providing meals and ingredients, and arranging food according to the needs of the users.

[0493] "Communication methods" refer to the technical mechanisms for exchanging data and information between various devices and systems.

[0494] This invention is a system that supports a healthy diet tailored to each individual. The server receives meal history and health data transmitted by the user and uses an AI algorithm to generate an individual nutritional profile based on this data. This AI model takes into particular consideration nutritional balance, individual food preferences, and allergy information. Based on the generated profile, the server creates a meal menu optimized for the individual's needs and transmits the menu information to the user's terminal via communication means.

[0495] The user's device uses a cross-platform mobile application built with React Native to browse menus and order meals in conjunction with the food service options offered. The backend system is built with Node.js and Express to manage user data and information from service providers. A MongoDB database enables the continuous accumulation of individual nutritional profiles and menu information.

[0496] As a concrete example, when a user enters their meal history and health checkup results through the application, the server analyzes for vitamin D deficiency and suggests "grilled salmon and spinach." The system then orders the dish through a partner restaurant via delivery service and delivers it to the user.

[0497] An example of a prompt for a generative AI model would be: "Analyze the user's dietary history to identify any nutritional deficiencies and suggest a menu if the user is deficient in vitamin D. However, the user has a shellfish allergy."

[0498] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0499] Step 1:

[0500] Users use their devices to input their meal history and health data.

[0501] The input data includes past meal history, health checkup results, and allergy information. The terminal sends this data to the server. As output, the user's individual data is stored on the server.

[0502] Step 2:

[0503] The server analyzes the received data to profile the individual's nutritional status and food preferences.

[0504] The input is data submitted by the user, and the output is an individual nutritional profile. The server uses AI algorithms to perform data analysis and profiling. Generative AI models are utilized in this process.

[0505] Step 3:

[0506] The server generates a meal plan that takes nutritional balance into consideration, based on the profiling results.

[0507] The input is a nutritional profile, and the output is a meal menu. The server inputs prompts into a generative model and automatically generates the menu.

[0508] Step 4:

[0509] The server sends the generated menu to the terminal.

[0510] As output, a personalized meal menu is displayed on the user's device. The user can select their preferred items from these menus.

[0511] Step 5:

[0512] Users select menu items via their terminal and place orders through the corresponding food service.

[0513] The input is the user's menu selection, and the output is the transmission of the selection information to the food service. The selected menu item is then ordered through integration with the service provider's system.

[0514] Step 6:

[0515] Users actually receive the food and provide feedback.

[0516] The input is feedback based on the experience of the provided dish, and the output is the transmission of this new feedback data to the server. This feedback will be used in the next menu generation process.

[0517] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0518] This invention relates to a system that provides personalized meal suggestions, taking into account a user's eating history, health data, and emotional state. The system aims to provide menus that satisfy not only the user's nutritional needs but also their emotional needs.

[0519] First, the device receives input from the user. At this time, the user inputs not only their meal history and health data, but also their emotional state in real time through an emotion engine. This emotional information is collected through voice and facial expression analysis.

[0520] The received data is sent to a server. The server analyzes the data using an AI algorithm and creates an individualized nutritional profile. This profile combines the user's daily eating habits, health status, and even emotional data.

[0521] Next, the server's algorithm generates a meal menu tailored to the user. This process considers not only nutritional balance but also ingredients and cooking methods that address the user's emotional state. For example, it might suggest a relaxing herbal tea when the user is under high stress.

[0522] The generated menu is then presented to the user again via the terminal. The menu includes detailed information such as necessary nutritional information, a list of items that can be purchased at convenience stores, and the effects of ingredients that correspond to emotions.

[0523] After users actually try the menu, they input their results and impressions into the system as feedback. This feedback is based on changes in the user's emotions and their satisfaction with the meal.

[0524] Finally, the server updates the AI ​​model based on the feedback received, improving the accuracy of future menu suggestions. In this way, the system is optimized for each user, supporting a highly satisfying eating lifestyle not only nutritionally but also emotionally.

[0525] As a concrete example, when user B logs in, it is discovered that their mood is unstable that day. As a result, the server suggests oatmeal, which has a calming effect, and a fruit salad containing vitamin C, which can boost mood. In this way, a comprehensive meal recommendation that takes emotional state into consideration is realized.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The device accepts input from the user. The user inputs their meal history, health data, and emotional information. The emotional information is processed in real time by an emotion engine, which analyzes voice and facial expressions via microphones and cameras to identify emotions.

[0529] Step 2:

[0530] The terminal encrypts all entered data before sending it to the server for security purposes. This ensures that data transfer is performed while protecting user privacy.

[0531] Step 3:

[0532] The server uses AI algorithms to analyze the received data. Based on meal history and health data, nutritional status is analyzed, and at the same time, an emotion engine evaluates the user's emotional state, creating an integrated nutritional profile.

[0533] Step 4:

[0534] The server generates meal plans based on the generated nutritional profile. It proposes individually optimized menus that incorporate nutritional balance, preferred ingredients, and specific foods and ingredients that suit the user's emotional state.

[0535] Step 5:

[0536] The server sends the created menu back to the terminal. The terminal provides the user with menu details, nutritional information, and reasoning behind ingredient selections based on emotions. A list of items available at convenience stores is also attached.

[0537] Step 6:

[0538] Users eat according to the provided meal menu and observe changes in their emotions and level of satisfaction. After finishing the meal, users input feedback about their satisfaction and emotional changes into the device.

[0539] Step 7:

[0540] The device securely sends feedback to the server. The server uses this feedback to update the AI ​​algorithm model and adapt it to further improve accuracy in future menu generation.

[0541] Through this series of steps, the system becomes capable of providing more personalized menus tailored to each user's unique nutritional status and emotional state.

[0542] (Example 2)

[0543] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0544] When suggesting meals, it is desirable to consider not only individual health conditions and food preferences but also emotional states. However, conventional systems have the challenge of making personalized suggestions that reflect emotions. Furthermore, in order to improve the accuracy and satisfaction of meal suggestions, a system that efficiently utilizes individual feedback is required.

[0545] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0546] In this invention, the server includes means for collecting an individual's eating history, health status, and emotional state; means for analyzing the collected data to profile the individual's nutritional status, food preferences, and emotional state; and means for updating an artificial intelligence model based on the individual's feedback. This enables a high level of personalization that takes into account the individual's health status, food preferences, and emotional trends, thereby increasing user satisfaction.

[0547] A "dietary history" is a collection of information about the types, times, and frequency of meals an individual has eaten in the past.

[0548] "Health status" refers to information about an individual's physical health, including medical history, allergy information, and anthropometric data.

[0549] "Emotional state" refers to data that indicates an individual's emotional condition, and includes emotional indicators such as stress levels and mood swings.

[0550] "Profiling" is the process of analyzing collected information to reveal individual characteristics and tendencies.

[0551] "Nutritional balance" refers to a state in which a meal contains all the nutrients the body needs in the appropriate proportions.

[0552] An "artificial intelligence model" is a computer program that analyzes data and performs predictions and classifications, and is a system that evolves based on learning algorithms.

[0553] "Feedback" refers to the reactions and opinions provided by users, and the system is improved and adjusted based on this feedback.

[0554] A specific description will be given of embodiments for carrying out this invention.

[0555] This system is designed to provide personalized meal suggestions to individual users. Users can input data on their meal history, health status, and emotional state using a device with a dedicated application installed. Emotional state is collected in real time by analyzing the user's voice and facial expressions. Smartphones and wearable devices are used as hardware for this process.

[0556] Data collected on the device is securely transmitted to the server. The server uses AI frameworks such as TensorFlow as data analysis software to analyze the collected information. As a result of the analysis, a user-specific nutrition profile is generated, which supports the selection of nutritional balance and ingredients that are appropriate for each individual user.

[0557] Furthermore, the server uses a generative AI model to generate customized meal menus from the nutritional profile. These generated menus are presented to the user via a terminal. The menus include detailed nutritional information, cooking instructions, and information on purchasing related products.

[0558] Users select and prepare their meals based on the provided menu. After the meal, users send feedback to the server through the application. The server updates its AI model based on the received feedback, improving the accuracy of future suggestions. This enables meal suggestions optimized for each user.

[0559] For example, by entering a prompt message into the server such as, "Please suggest a suitable meal menu when the user is feeling stressed," an appropriate menu will be generated. In this way, the system effectively functions to enhance satisfaction and emotional fulfillment related to food.

[0560] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0561] Step 1:

[0562] The device collects data from the user. The user inputs their meal history, health status, and emotional state through the application. Emotional states are captured in real time using voice and facial expression analysis technology. The input data is converted to an appropriate format, such as JSON, on the device.

[0563] Step 2:

[0564] The device sends the collected data to the server. The data is encrypted using the HTTPS protocol to ensure security during transfer to the cloud server. This data transfer allows the server to receive detailed user information.

[0565] Step 3:

[0566] The server analyzes the data it receives. Based on this data, an AI algorithm is used to profile the individual's nutritional status, food preferences, and emotional state. Specifically, a machine learning model runs on the TensorFlow framework to learn the user's eating and emotional patterns. As a result of the analysis, a user-specific nutritional profile is generated.

[0567] Step 4:

[0568] The server uses an AI model to create personalized meal plans. Based on the generated nutritional profile, it constructs menus that are nutritionally balanced and considerate of emotional needs. In this process, appropriate ingredients and recipes are selected from the database, and the proposed meal plan is completed.

[0569] Step 5:

[0570] The server generates a menu and sends it to the terminal. The terminal then presents it to the user. The menu displayed in the user interface includes detailed nutritional information for the dishes, cooking instructions, and a list of available products. The user can then choose and prepare their actual meal based on this information.

[0571] Step 6:

[0572] The system collects user feedback on their meal experience via a device. Users input their thoughts and satisfaction level after the meal using the application. The entered feedback data is then sent back to the server from the device.

[0573] Step 7:

[0574] The server updates the AI ​​model based on the feedback it receives. The machine learning model is then readjusted based on the new feedback data to improve prediction accuracy. This will result in future meal recommendations being more tailored to the user.

[0575] (Application Example 2)

[0576] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0577] Conventional meal suggestion systems only consider an individual's nutritional status and food preferences, failing to reflect their emotional state and making it difficult to support a satisfying diet for each user. Furthermore, they lack concrete means for users to determine whether the suggested menu is actually available for purchase or use, resulting in a lot of hassle for users before they can implement the suggestions.

[0578] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0579] In this invention, the server includes means for collecting an individual's meal history, health data, and emotional information; means for analyzing the collected data to profile the individual's nutritional status, food preferences, and emotional state; and means for generating a meal menu that matches the nutritional balance, personal preferences, and emotional state based on the profiling results, and further automatically issuing delivery instructions according to the meal menu. This enables personalized meal suggestions that are tailored to the emotional needs of each user and supports the immediate execution of the suggested menu.

[0580] A "dietary history" is a record of the foods an individual has consumed in the past, and is fundamental information for analyzing nutritional intake patterns and food preferences.

[0581] "Health data" refers to data that indicates an individual's physical health status, and includes information such as medical records and self-reported health indicators.

[0582] "Emotional information" refers to data that indicates an individual's psychological state and emotional changes, and is collected through voice analysis and facial expression analysis.

[0583] "Profiling" is the process of analyzing an individual's characteristics based on collected data, revealing traits such as nutritional status, food preferences, and emotional state.

[0584] A "meal menu" refers to the specific contents of a meal proposed to an individual, taking into consideration nutritional balance and ingredients and cooking methods that address their emotional state.

[0585] "Feedback" refers to users recording their satisfaction levels and opinions on suggested meal menus, providing valuable information to improve the accuracy of future suggestions.

[0586] A "delivery instruction" is a command that automatically handles the purchase and delivery procedures based on the suggested meal menu, and is intended to quickly provide ingredients and prepared meals to the user.

[0587] This system collects personal data via the user's smart device, analyzes it, and then provides meal suggestions. Specifically, devices such as smartphones and smart glasses collect the user's meal history, health data, and emotional information in real time. This emotional information is obtained through voice and facial expression analysis. The device then transmits this data to a server.

[0588] The server analyzes collected data using AI algorithms to profile the user's nutritional status, food preferences, and emotional state. This utilizes the Google Cloud Speech-to-Text API for speech analysis and OpenCV for image analysis. Based on the profiling results, the server generates meal menus tailored to the user's nutritional balance and emotional state. TensorFlow is used as the AI ​​model for creating user profiles and optimizing meal suggestions.

[0589] Furthermore, if the proposed menu is approved, the server uses an API to automate delivery instructions and connects with a food delivery service. This ensures that ingredients and meals are delivered to the user at the appropriate time.

[0590] This process enables comprehensive meal suggestions tailored to the individual user's emotional needs. For example, if a user is detected to be experiencing mild stress in the afternoon, the system will suggest relaxing beverages and snacks and arrange for them to be delivered immediately from a nearby cafe.

[0591] An example of a prompt message is: "Design a system that assesses the user's emotional state and suggests food and drink menus to reduce stress. Input data will include the user's voice and facial expression images." The aim is to improve user satisfaction by adding emotional adaptability to food selection.

[0592] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0593] Step 1:

[0594] The device receives input from the user. This input includes meal history, health data, and emotional information collected through voice and facial expressions. The input data is preprocessed for sentiment analysis, and voice data is converted to text by the Google Cloud Speech-to-Text API.

[0595] Step 2:

[0596] The terminal sends pre-processed data to the server. Input data, including text data converted from speech, health data, and image data for sentiment analysis, is delivered to the server, which then uses this data to perform analysis.

[0597] Step 3:

[0598] The server analyzes the received data using an AI algorithm to generate a profile of the user's nutritional status, food preferences, and emotional state. Here, TensorFlow is used to create a unique profile from various data and extract specific features corresponding to the user's health and emotions.

[0599] Step 4:

[0600] The server creates meal menus based on the generated profiles. The menus are designed using ingredients and cooking methods that take into account nutritional balance and emotional state. This stage includes emotional support, such as suggesting relaxing ingredients when stress levels are high.

[0601] Step 5:

[0602] The server returns the created menu to the terminal and presents it to the user. The user reviews the menu and, upon selection, the order is executed. The user's selection is registered as input, and the meal menu to be served is determined as a result.

[0603] Step 6:

[0604] After receiving user approval, the server automatically sends delivery instructions to the food delivery service. Using an API, the delivery process is automated based on available product information, and ingredients and meals are prepared to be delivered to the user.

[0605] Step 7:

[0606] After receiving their meal, users input feedback into the system. The collected feedback is sent to the system, and the server uses it to generate the next menu. Based on the collected feedback, the AI ​​model is updated, improving the accuracy of future suggestions.

[0607] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0608] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0609] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0610] [Fourth Embodiment]

[0611] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0612] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0613] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0614] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0615] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0616] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0617] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0618] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0619] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0620] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0621] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0622] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0623] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0624] This invention is a system that provides meal suggestions that take into account an individual's nutritional status and preferences. The specific operation of the system is described below.

[0625] First, the terminal serves as a receiver for user input. Users input their eating history, health checkup results, allergy information, and other data into the system via the terminal. This information becomes the basic data for analyzing an individual's nutritional status and food preferences.

[0626] Next, this input data is sent to the server. Based on the received data, the server uses an AI algorithm to create an individual nutrition profile. The nutrition profile includes the user's past eating habits, nutrient deficiencies or excesses, and allergy risks.

[0627] The AI ​​model automatically generates personalized meal plans based on this nutritional profile. The generated menus consider a healthy nutritional balance and are tailored to the user's preferences and allergy information.

[0628] Next, the generated menu is sent to the terminal and presented to the user. The user can then procure ingredients and cook according to the presented menu. The menu also includes nutritional information and information on potential convenience store items to support smooth ingredient procurement.

[0629] After a user tries a menu item, they input their results and impressions as feedback. This feedback is sent back to the server, and the system uses this information to adjust the AI ​​algorithm. This is to enable the system to suggest more personalized content for future menu generation.

[0630] As a concrete example, when user A uses the system, the server analyzes the user's dietary history to determine that they are lacking protein and suggests a "healthy bowl using tofu." Furthermore, depending on whether or not the user has a dairy allergy, alternative dairy-free products are added to their shopping list at the convenience store.

[0631] In this way, the system suggests meals best suited to individual circumstances, helping users easily maintain a healthy diet. This invention provides a groundbreaking means of conveniently realizing a healthy diet for people.

[0632] The following describes the processing flow.

[0633] Step 1:

[0634] Users log in to their devices and enter their meal history, health checkup results, and allergy information. This entered data is linked to the user's individual account.

[0635] Step 2:

[0636] The terminal sends the entered data to the system's server. The data is encrypted and transmitted securely.

[0637] Step 3:

[0638] The server stores the received data in a database and analyzes it using AI algorithms. Specifically, it profiles the user's nutritional status and food preferences to identify nutrient deficiencies and excesses.

[0639] Step 4:

[0640] Based on the analysis results, the server generates a nutritionally balanced meal menu suitable for the user. The algorithm constructs the menu considering the nutritional value of ingredients, the user's preferences, and allergy information.

[0641] Step 5:

[0642] The server sends the generated menu to the terminal. The transmitted data includes nutritional information and a list of related products that can be purchased at convenience stores.

[0643] Step 6:

[0644] The terminal displays the received menu information to the user. The user reviews the displayed menu, purchases ingredients as needed, and prepares the meal.

[0645] Step 7:

[0646] After the user tries the suggested menu items, they input feedback about the results and their satisfaction level into the device.

[0647] Step 8:

[0648] The device sends user feedback to the server. Based on this feedback, the server updates the AI ​​model to improve accuracy in future menu generation.

[0649] (Example 1)

[0650] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0651] In modern society, it is extremely difficult to propose nutritionally balanced meals tailored to individual health conditions and preferences. Moreover, for people leading busy lives, it is difficult to easily obtain information on how to select and purchase appropriate ingredients. As a result, there is a growing problem of an increasing number of people not getting enough nutrients necessary for maintaining good health. Furthermore, those with allergies face the risk of accidentally consuming dangerous ingredients. Therefore, there is a need for a system that can automatically generate appropriate meal menus, taking into account an individual's nutritional status and food preferences, and provide the necessary information.

[0652] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0653] In this invention, the server includes means for acquiring meal history and health information, means for analyzing the acquired data to design a nutritional profile, and means for generating meal menus using a generation AI model based on the nutritional profile. This enables the automatic provision of nutritionally balanced meal menus tailored to individual needs, thereby realizing a healthy lifestyle.

[0654] "Individual" refers to a specific person, and the subject of creating a nutritional profile based on that person's dietary history and health information.

[0655] A "nutritional profile" is a dataset that comprehensively includes information on nutrient deficiencies and excesses, food preferences, and allergies, analyzed using an individual's dietary history and health information.

[0656] A "generative AI model" refers to an algorithm that automatically generates meal menus that are balanced nutritionally and tailored to individual preferences, based on an individual's nutritional profile.

[0657] A "meal menu" refers to a personalized list of specific foods and dishes created by a generative AI model, taking into account nutritional value and personal preferences.

[0658] "Responses" refer to the opinions and evaluations that individuals give regarding the presented meal menu, and this information is collected to help create future menus.

[0659] "Product information" refers to detailed data about purchasable products related to meal menus, and is information that can be used as a reference when individuals procure appropriate ingredients.

[0660] This invention is a system that proposes customized meal menus based on an individual's nutritional status and food preferences. Specific embodiments of the system are described below.

[0661] The terminal functions as a device for receiving data input from users. Users input their meal history, health checkup data, and allergy information through an interface on the terminal. This interface is implemented using a dedicated application or web-based form and is designed to be easy for users to use. The data entered from the terminal is converted into a structured data format, such as JSON format, and sent to the server using a secure protocol.

[0662] The server processes the received data and performs analysis through AI algorithms. Specifically, the AI ​​model is developed in Python and utilizes libraries such as TensorFlow or PyTorch. This model generates a nutritional profile and creates menus that reflect the user's eating habits, nutrient deficiencies or excesses, and allergy information. At this time, the server accesses a database to refer to the latest nutritional data and ingredient information.

[0663] The generated menus are tailored to nutritional balance and individual preferences, and also include information on available products. For example, a user lacking protein might be suggested a "nutritious dish using tofu," and alternative ingredients would be suggested based on allergy information. As a concrete example, a prompt used in the generating AI model might be something like, "Generate a recommended meal plan for the next week based on the user's eating history and health data."

[0664] Finally, the generated menu is sent back to the terminal. The user can immediately view the menu and efficiently procure the necessary ingredients by referring to the purchasing information associated with the menu.

[0665] In this way, the system provides meal suggestions tailored to specific users, supporting them in achieving a healthy diet.

[0666] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0667] Step 1:

[0668] The terminal receives input data from the user. Here, the user enters their meal history, health status information, and allergy information via an application or web interface. This input data is processed into JSON format. The final output is structured data that is securely sent to the server.

[0669] Step 2:

[0670] The server receives data sent from the terminal. The server parses the received JSON data and starts creating a nutritional profile by comparing it with the database. Based on the input data, it calculates nutrient deficiencies or excesses and allergy risks. The output of this analysis is a detailed dataset that constitutes the individual's nutritional profile.

[0671] Step 3:

[0672] The server's AI algorithm uses a generated AI model to receive a nutritional profile as input and generate a meal plan suitable for the user. This process uses prompts to generate the menu. For example, the prompt might be "Create a healthy one-week menu based on user A's nutritional status." The output is a meal plan that balances nutritional value with personal preferences.

[0673] Step 4:

[0674] The server sends the generated menu to the terminal in structured data format. The terminal parses the received data and displays it in a user-friendly interface. As output, the user can view a detailed meal menu and related purchase information.

[0675] Step 5:

[0676] After the user prepares the meal according to the provided menu, they input feedback into the system via their device. This feedback includes their impressions of the dish they actually prepared and suggestions for improvement. The device then processes this information back into JSON format and sends it to the server.

[0677] Step 6:

[0678] The server analyzes user feedback and incorporates it into the AI ​​algorithm to improve the system's accuracy. Based on the input feedback, adjustments are made to inform the next menu suggestion. This output is adjustment data, including suggestions for improvement for the next suggestion.

[0679] (Application Example 1)

[0680] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0681] In today's busy lifestyle, selecting and preparing appropriate meals based on individual health conditions and nutritional balance is difficult for many people. As a result, health problems such as nutritional excess, deficiency, and allergic reactions are common. Furthermore, finding meals that suit individual preferences is not easy. There is a need to solve these problems and provide healthy meals that are tailored to each individual.

[0682] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0683] In this invention, the server includes means for collecting an individual's dietary history and health data, means for analyzing the collected data to profile the individual's nutritional status and food preferences, and means for generating a meal menu that matches nutritional balance and individual preferences based on the profiling results. This makes it possible to select and provide meals that meet individual needs.

[0684] "Dietary history" refers to information that records the content, frequency, and timing of meals an individual has consumed in the past.

[0685] "Health data" refers to information that represents an individual's health status, and includes results from health checkups and daily health management data.

[0686] "Profiling" refers to the process of analyzing collected data to understand an individual's preferences and tendencies, and then creating a model based on that understanding.

[0687] "Nutritional balance" refers to a state in which the human body is consuming the necessary nutrients in appropriate proportions.

[0688] A "meal menu" is a list that shows the contents and menu of a meal, and is structured according to a specific purpose or condition.

[0689] "Meal service" refers to the business or activity of providing meals and ingredients, and arranging food according to the needs of the users.

[0690] "Communication methods" refer to the technical mechanisms for exchanging data and information between various devices and systems.

[0691] This invention is a system that supports a healthy diet tailored to each individual. The server receives meal history and health data transmitted by the user and uses an AI algorithm to generate an individual nutritional profile based on this data. This AI model takes into particular consideration nutritional balance, individual food preferences, and allergy information. Based on the generated profile, the server creates a meal menu optimized for the individual's needs and transmits the menu information to the user's terminal via communication means.

[0692] The user's device uses a cross-platform mobile application built with React Native to browse menus and order meals in conjunction with the food service options offered. The backend system is built with Node.js and Express to manage user data and information from service providers. A MongoDB database enables the continuous accumulation of individual nutritional profiles and menu information.

[0693] As a concrete example, when a user enters their meal history and health checkup results through the application, the server analyzes for vitamin D deficiency and suggests "grilled salmon and spinach." The system then orders the dish through a partner restaurant via delivery service and delivers it to the user.

[0694] An example of a prompt for a generative AI model would be: "Analyze the user's dietary history to identify any nutritional deficiencies and suggest a menu if the user is deficient in vitamin D. However, the user has a shellfish allergy."

[0695] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0696] Step 1:

[0697] Users use their devices to input their meal history and health data.

[0698] The input data includes past meal history, health checkup results, and allergy information. The terminal sends this data to the server. As output, the user's individual data is stored on the server.

[0699] Step 2:

[0700] The server analyzes the received data to profile the individual's nutritional status and food preferences.

[0701] The input is data submitted by the user, and the output is an individual nutritional profile. The server uses AI algorithms to perform data analysis and profiling. Generative AI models are utilized in this process.

[0702] Step 3:

[0703] The server generates a meal plan that takes nutritional balance into consideration, based on the profiling results.

[0704] The input is a nutritional profile, and the output is a meal menu. The server inputs prompts into a generative model and automatically generates the menu.

[0705] Step 4:

[0706] The server sends the generated menu to the terminal.

[0707] As output, a personalized meal menu is displayed on the user's device. The user can select their preferred items from these menus.

[0708] Step 5:

[0709] Users select menu items via their terminal and place orders through the corresponding food service.

[0710] The input is the user's menu selection, and the output is the transmission of the selection information to the food service. The selected menu item is then ordered through integration with the service provider's system.

[0711] Step 6:

[0712] Users actually receive the food and provide feedback.

[0713] The input is feedback based on the experience of the provided dish, and the output is the transmission of this new feedback data to the server. This feedback will be used in the next menu generation process.

[0714] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0715] This invention relates to a system that provides personalized meal suggestions, taking into account a user's eating history, health data, and emotional state. The system aims to provide menus that satisfy not only the user's nutritional needs but also their emotional needs.

[0716] First, the device receives input from the user. At this time, the user inputs not only their meal history and health data, but also their emotional state in real time through an emotion engine. This emotional information is collected through voice and facial expression analysis.

[0717] The received data is sent to a server. The server analyzes the data using an AI algorithm and creates an individualized nutritional profile. This profile combines the user's daily eating habits, health status, and even emotional data.

[0718] Next, the server's algorithm generates a meal menu tailored to the user. This process considers not only nutritional balance but also ingredients and cooking methods that address the user's emotional state. For example, it might suggest a relaxing herbal tea when the user is under high stress.

[0719] The generated menu is then presented to the user again via the terminal. The menu includes detailed information such as necessary nutritional information, a list of items that can be purchased at convenience stores, and the effects of ingredients that correspond to emotions.

[0720] After users actually try the menu, they input their results and impressions into the system as feedback. This feedback is based on changes in the user's emotions and their satisfaction with the meal.

[0721] Finally, the server updates the AI ​​model based on the feedback received, improving the accuracy of future menu suggestions. In this way, the system is optimized for each user, supporting a highly satisfying eating lifestyle not only nutritionally but also emotionally.

[0722] As a concrete example, when user B logs in, it is discovered that their mood is unstable that day. As a result, the server suggests oatmeal, which has a calming effect, and a fruit salad containing vitamin C, which can boost mood. In this way, a comprehensive meal recommendation that takes emotional state into consideration is realized.

[0723] The following describes the processing flow.

[0724] Step 1:

[0725] The device accepts input from the user. The user inputs their meal history, health data, and emotional information. The emotional information is processed in real time by an emotion engine, which analyzes voice and facial expressions via microphones and cameras to identify emotions.

[0726] Step 2:

[0727] The terminal encrypts all entered data before sending it to the server for security purposes. This ensures that data transfer is performed while protecting user privacy.

[0728] Step 3:

[0729] The server uses AI algorithms to analyze the received data. Based on meal history and health data, nutritional status is analyzed, and at the same time, an emotion engine evaluates the user's emotional state, creating an integrated nutritional profile.

[0730] Step 4:

[0731] The server generates meal plans based on the generated nutritional profile. It proposes individually optimized menus that incorporate nutritional balance, preferred ingredients, and specific foods and ingredients that suit the user's emotional state.

[0732] Step 5:

[0733] The server sends the created menu back to the terminal. The terminal provides the user with menu details, nutritional information, and reasoning behind ingredient selections based on emotions. A list of items available at convenience stores is also attached.

[0734] Step 6:

[0735] Users eat according to the provided meal menu and observe changes in their emotions and level of satisfaction. After finishing the meal, users input feedback about their satisfaction and emotional changes into the device.

[0736] Step 7:

[0737] The device securely sends feedback to the server. The server uses this feedback to update the AI ​​algorithm model and adapt it to further improve accuracy in future menu generation.

[0738] Through this series of steps, the system becomes capable of providing more personalized menus tailored to each user's unique nutritional status and emotional state.

[0739] (Example 2)

[0740] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0741] When suggesting meals, it is desirable to consider not only individual health conditions and food preferences but also emotional states. However, conventional systems have the challenge of making personalized suggestions that reflect emotions. Furthermore, in order to improve the accuracy and satisfaction of meal suggestions, a system that efficiently utilizes individual feedback is required.

[0742] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0743] In this invention, the server includes means for collecting an individual's eating history, health status, and emotional state; means for analyzing the collected data to profile the individual's nutritional status, food preferences, and emotional state; and means for updating an artificial intelligence model based on the individual's feedback. This enables a high level of personalization that takes into account the individual's health status, food preferences, and emotional trends, thereby increasing user satisfaction.

[0744] A "dietary history" is a collection of information about the types, times, and frequency of meals an individual has eaten in the past.

[0745] "Health status" refers to information about an individual's physical health, including medical history, allergy information, and anthropometric data.

[0746] "Emotional state" refers to data that indicates an individual's emotional condition, and includes emotional indicators such as stress levels and mood swings.

[0747] "Profiling" is the process of analyzing collected information to reveal individual characteristics and tendencies.

[0748] "Nutritional balance" refers to a state in which a meal contains all the nutrients the body needs in the appropriate proportions.

[0749] An "artificial intelligence model" is a computer program that analyzes data and performs predictions and classifications, and is a system that evolves based on learning algorithms.

[0750] "Feedback" refers to the reactions and opinions provided by users, and the system is improved and adjusted based on this feedback.

[0751] A specific description will be given of embodiments for carrying out this invention.

[0752] This system is designed to provide personalized meal suggestions to individual users. Users can input data on their meal history, health status, and emotional state using a device with a dedicated application installed. Emotional state is collected in real time by analyzing the user's voice and facial expressions. Smartphones and wearable devices are used as hardware for this process.

[0753] Data collected on the device is securely transmitted to the server. The server uses AI frameworks such as TensorFlow as data analysis software to analyze the collected information. As a result of the analysis, a user-specific nutrition profile is generated, which supports the selection of nutritional balance and ingredients that are appropriate for each individual user.

[0754] Furthermore, the server uses a generative AI model to generate customized meal menus from the nutritional profile. These generated menus are presented to the user via a terminal. The menus include detailed nutritional information, cooking instructions, and information on purchasing related products.

[0755] Users select and prepare their meals based on the provided menu. After the meal, users send feedback to the server through the application. The server updates its AI model based on the received feedback, improving the accuracy of future suggestions. This enables meal suggestions optimized for each user.

[0756] For example, by entering a prompt message into the server such as, "Please suggest a suitable meal menu when the user is feeling stressed," an appropriate menu will be generated. In this way, the system effectively functions to enhance satisfaction and emotional fulfillment related to food.

[0757] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0758] Step 1:

[0759] The device collects data from the user. The user inputs their meal history, health status, and emotional state through the application. Emotional states are captured in real time using voice and facial expression analysis technology. The input data is converted to an appropriate format, such as JSON, on the device.

[0760] Step 2:

[0761] The device sends the collected data to the server. The data is encrypted using the HTTPS protocol to ensure security during transfer to the cloud server. This data transfer allows the server to receive detailed user information.

[0762] Step 3:

[0763] The server analyzes the data it receives. Based on this data, an AI algorithm is used to profile the individual's nutritional status, food preferences, and emotional state. Specifically, a machine learning model runs on the TensorFlow framework to learn the user's eating and emotional patterns. As a result of the analysis, a user-specific nutritional profile is generated.

[0764] Step 4:

[0765] The server uses an AI model to create personalized meal plans. Based on the generated nutritional profile, it constructs menus that are nutritionally balanced and considerate of emotional needs. In this process, appropriate ingredients and recipes are selected from the database, and the proposed meal plan is completed.

[0766] Step 5:

[0767] The server generates a menu and sends it to the terminal. The terminal then presents it to the user. The menu displayed in the user interface includes detailed nutritional information for the dishes, cooking instructions, and a list of available products. The user can then choose and prepare their actual meal based on this information.

[0768] Step 6:

[0769] The system collects user feedback on their meal experience via a device. Users input their thoughts and satisfaction level after the meal using the application. The entered feedback data is then sent back to the server from the device.

[0770] Step 7:

[0771] The server updates the AI ​​model based on the feedback it receives. The machine learning model is then readjusted based on the new feedback data to improve prediction accuracy. This will result in future meal recommendations being more tailored to the user.

[0772] (Application Example 2)

[0773] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0774] Conventional meal suggestion systems only consider an individual's nutritional status and food preferences, failing to reflect their emotional state and making it difficult to support a satisfying diet for each user. Furthermore, they lack concrete means for users to determine whether the suggested menu is actually available for purchase or use, resulting in a lot of hassle for users before they can implement the suggestions.

[0775] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0776] In this invention, the server includes means for collecting an individual's meal history, health data, and emotional information; means for analyzing the collected data to profile the individual's nutritional status, food preferences, and emotional state; and means for generating a meal menu that matches the nutritional balance, personal preferences, and emotional state based on the profiling results, and further automatically issuing delivery instructions according to the meal menu. This enables personalized meal suggestions that are tailored to the emotional needs of each user and supports the immediate execution of the suggested menu.

[0777] A "dietary history" is a record of the foods an individual has consumed in the past, and is fundamental information for analyzing nutritional intake patterns and food preferences.

[0778] "Health data" refers to data that indicates an individual's physical health status, and includes information such as medical records and self-reported health indicators.

[0779] "Emotional information" refers to data that indicates an individual's psychological state and emotional changes, and is collected through voice analysis and facial expression analysis.

[0780] "Profiling" is the process of analyzing an individual's characteristics based on collected data, revealing traits such as nutritional status, food preferences, and emotional state.

[0781] A "meal menu" refers to the specific contents of a meal proposed to an individual, taking into consideration nutritional balance and ingredients and cooking methods that address their emotional state.

[0782] "Feedback" refers to users recording their satisfaction levels and opinions on suggested meal menus, providing valuable information to improve the accuracy of future suggestions.

[0783] A "delivery instruction" is a command that automatically handles the purchase and delivery procedures based on the suggested meal menu, and is intended to quickly provide ingredients and prepared meals to the user.

[0784] This system collects personal data via the user's smart device, analyzes it, and then provides meal suggestions. Specifically, devices such as smartphones and smart glasses collect the user's meal history, health data, and emotional information in real time. This emotional information is obtained through voice and facial expression analysis. The device then transmits this data to a server.

[0785] The server analyzes collected data using AI algorithms to profile the user's nutritional status, food preferences, and emotional state. This utilizes the Google Cloud Speech-to-Text API for speech analysis and OpenCV for image analysis. Based on the profiling results, the server generates meal menus tailored to the user's nutritional balance and emotional state. TensorFlow is used as the AI ​​model for creating user profiles and optimizing meal suggestions.

[0786] Furthermore, if the proposed menu is approved, the server uses an API to automate delivery instructions and connects with a food delivery service. This ensures that ingredients and meals are delivered to the user at the appropriate time.

[0787] This process enables comprehensive meal suggestions tailored to the individual user's emotional needs. For example, if a user is detected to be experiencing mild stress in the afternoon, the system will suggest relaxing beverages and snacks and arrange for them to be delivered immediately from a nearby cafe.

[0788] An example of a prompt message is: "Design a system that assesses the user's emotional state and suggests food and drink menus to reduce stress. Input data will include the user's voice and facial expression images." The aim is to improve user satisfaction by adding emotional adaptability to food selection.

[0789] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0790] Step 1:

[0791] The device receives input from the user. This input includes meal history, health data, and emotional information collected through voice and facial expressions. The input data is preprocessed for sentiment analysis, and voice data is converted to text by the Google Cloud Speech-to-Text API.

[0792] Step 2:

[0793] The terminal sends pre-processed data to the server. Input data, including text data converted from speech, health data, and image data for sentiment analysis, is delivered to the server, which then uses this data to perform analysis.

[0794] Step 3:

[0795] The server analyzes the received data using an AI algorithm to generate a profile of the user's nutritional status, food preferences, and emotional state. Here, TensorFlow is used to create a unique profile from various data and extract specific features corresponding to the user's health and emotions.

[0796] Step 4:

[0797] The server creates meal menus based on the generated profiles. The menus are designed using ingredients and cooking methods that take into account nutritional balance and emotional state. This stage includes emotional support, such as suggesting relaxing ingredients when stress levels are high.

[0798] Step 5:

[0799] The server returns the created menu to the terminal and presents it to the user. The user reviews the menu and, upon selection, the order is executed. The user's selection is registered as input, and the meal menu to be served is determined as a result.

[0800] Step 6:

[0801] After receiving user approval, the server automatically sends delivery instructions to the food delivery service. Using an API, the delivery process is automated based on available product information, and ingredients and meals are prepared to be delivered to the user.

[0802] Step 7:

[0803] After receiving their meal, users input feedback into the system. The collected feedback is sent to the system, and the server uses it to generate the next menu. Based on the collected feedback, the AI ​​model is updated, improving the accuracy of future suggestions.

[0804] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0805] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0806] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0807] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0808] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0809] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0810] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0811] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0812] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0813] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0814] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0815] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0816] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0817] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0818] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0819] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0820] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0821] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0822] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0823] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0824] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0825] The following is further disclosed regarding the embodiments described above.

[0826] (Claim 1)

[0827] Means for collecting personal dietary history and health data,

[0828] A means for analyzing the collected data to profile an individual's nutritional status and food preferences,

[0829] A means for generating a meal menu that matches nutritional balance and individual preferences based on the aforementioned profiling results,

[0830] A means for presenting the generated menu to an individual,

[0831] A means of collecting individual feedback on the aforementioned menu and utilizing it for generating the next menu,

[0832] A system that includes this.

[0833] (Claim 2)

[0834] The system according to claim 1, wherein the menu presented to the individual takes into account nutritional guidelines and allergy information.

[0835] (Claim 3)

[0836] The system according to claim 1, wherein the presented menu includes information on products that can be purchased.

[0837] "Example 1"

[0838] (Claim 1)

[0839] A device that acquires an individual's dietary history and health information,

[0840] A device for analyzing the acquired data and designing an individual's nutritional profile,

[0841] A device that generates meal menus tailored to nutritional balance and individual preferences using a generation AI model based on the aforementioned nutrition profile,

[0842] A device for communicating the generated meal menu to an individual,

[0843] A device that obtains individual responses to the aforementioned communicated menu and utilizes them in creating the next menu,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, wherein the menu communicated to the individual takes into account nutritional guidelines and allergy information.

[0847] (Claim 3)

[0848] The system according to claim 1, wherein the transmitted menu includes information on products available for purchase.

[0849] "Application Example 1"

[0850] (Claim 1)

[0851] Means for collecting personal dietary history and health data,

[0852] A means for analyzing the collected data to profile an individual's nutritional status and food preferences,

[0853] A means for generating a meal menu that matches nutritional balance and individual preferences based on the aforementioned profiling results,

[0854] A means of presenting the generated menu to an individual and providing a meal in conjunction with selectable meal services,

[0855] A means of collecting individual feedback on the aforementioned menu and utilizing it for generating the next menu,

[0856] A communication means for providing integrated meal services based on the aforementioned menu,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, which proposes a menu to the aforementioned individual that takes into account nutritional guidelines and allergy information, and offers corresponding services.

[0860] (Claim 3)

[0861] The system according to claim 1, wherein the presented menu includes information on purchasable products and provides the selected meal in a manner that allows for immediate supply.

[0862] "Example 2 of combining an emotion engine"

[0863] (Claim 1)

[0864] Means for collecting an individual's dietary history, health status, and emotional state,

[0865] A means for analyzing the collected data to profile an individual's nutritional status, food preferences, and emotional state,

[0866] A means for generating a meal menu that matches nutritional balance, personal preferences, and emotional state based on the aforementioned profiling results,

[0867] A means for presenting the generated menu to an individual,

[0868] A means of collecting individual feedback on the aforementioned menu and utilizing it for generating the next menu,

[0869] A means for updating the artificial intelligence model based on the aforementioned individual's feedback,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, wherein the menu presented to the individual takes into account nutritional standards and responses to specific foods.

[0873] (Claim 3)

[0874] The system according to claim 1, wherein the presented menu includes information on purchasable products and cooking methods.

[0875] "Application example 2 when combining with an emotional engine"

[0876] (Claim 1)

[0877] Means for collecting personal dietary history, health data, and emotional information,

[0878] A means for analyzing the collected data to profile an individual's nutritional status, food preferences, and emotional state,

[0879] A means for generating a meal menu that matches nutritional balance, personal preferences, and emotional state based on the aforementioned profiling results,

[0880] A means for presenting the generated menu to an individual,

[0881] A means of collecting individual feedback on the aforementioned menu and utilizing it for generating the next menu,

[0882] A means for automatically issuing delivery instructions according to the aforementioned meal menu,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, wherein the menu presented to the individual takes into account nutritional guidelines and allergy information, and includes effects corresponding to the individual's emotional state.

[0886] (Claim 3)

[0887] The system according to claim 1, wherein the presented menu includes information on purchasable products and delivery information. [Explanation of symbols]

[0888] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for collecting personal dietary history and health data, A means for analyzing the collected data to profile an individual's nutritional status and food preferences, A means for generating a meal menu that matches nutritional balance and individual preferences based on the aforementioned profiling results, A means for presenting the generated menu to an individual, A means of collecting individual feedback on the aforementioned menu and utilizing it for generating the next menu, A system that includes this.

2. The system according to claim 1, wherein the menu presented to the individual takes into account nutritional guidelines and allergy information.

3. The system according to claim 1, wherein the presented menu includes information on products that can be purchased.

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